Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China
Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the pot...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/12/2065 |
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| author | Feng Xu Zhaofu Li Shuyu Zhang Naitao Huang Zongyao Quan Wenmin Zhang Xiaojun Liu Xiaosan Jiang Jianjun Pan Alexander V. Prishchepov |
| author_facet | Feng Xu Zhaofu Li Shuyu Zhang Naitao Huang Zongyao Quan Wenmin Zhang Xiaojun Liu Xiaosan Jiang Jianjun Pan Alexander V. Prishchepov |
| author_sort | Feng Xu |
| collection | DOAJ |
| description | Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere. |
| first_indexed | 2024-03-10T18:52:41Z |
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| id | doaj.art-c814d85b1d0a49a79ebbe338ca9f910f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T18:52:41Z |
| publishDate | 2020-06-01 |
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| spelling | doaj.art-c814d85b1d0a49a79ebbe338ca9f910f2023-11-20T05:03:35ZengMDPI AGRemote Sensing2072-42922020-06-011212206510.3390/rs12122065Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, ChinaFeng Xu0Zhaofu Li1Shuyu Zhang2Naitao Huang3Zongyao Quan4Wenmin Zhang5Xiaojun Liu6Xiaosan Jiang7Jianjun Pan8Alexander V. Prishchepov9College of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaSchool of Geography, Nanjing Normal University, Nanjing 210023, ChinaCollege of Agriculture, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaCollege of Resources and Environmental Sciences, Nanjing Agricultural University, Nanjing 210095, ChinaDepartment of Geosciences and Natural Resource Management, University of Copenhagen, 1350 Copenhagen, DenmarkWinter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.https://www.mdpi.com/2072-4292/12/12/2065multi-temporalwinter wheattemporal aggregationGoogle earth enginecrop development phase |
| spellingShingle | Feng Xu Zhaofu Li Shuyu Zhang Naitao Huang Zongyao Quan Wenmin Zhang Xiaojun Liu Xiaosan Jiang Jianjun Pan Alexander V. Prishchepov Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China Remote Sensing multi-temporal winter wheat temporal aggregation Google earth engine crop development phase |
| title | Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China |
| title_full | Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China |
| title_fullStr | Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China |
| title_full_unstemmed | Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China |
| title_short | Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China |
| title_sort | mapping winter wheat with combinations of temporally aggregated sentinel 2 and landsat 8 data in shandong province china |
| topic | multi-temporal winter wheat temporal aggregation Google earth engine crop development phase |
| url | https://www.mdpi.com/2072-4292/12/12/2065 |
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